Categoría: Projects

This project analyze the shortcomings of using IRM-based benchmarkings to evaluate cloud key-value stores. Also propose a trace replay model suitable for these systems, and develop KV-replay, an open-source replayer that implements this model.

The goal is to improve storage workload modeling via unsupervised clustering of stochastic processes, with the goal of synthetic workload generation to improve the state-of-the-art in benchmarking and simulation based evaluations. This project is funded through a Google Faculty Research Award

We are working on building improved distributed storage systems (object stores, file systems, key-value stores). Our work is mostly focused on dynamic and autonomic methods that can be used by storage systems to maximize performance as the workload changes, adapting to client demands.

Improving cacao flavour can be done controlling the fermentation process, in this project we study how to improve the fermentation process using new technologies.Team: Juan Cevallos,Daniel Ochoa, Ronald Criollo,Owaldo Bayona